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Prompt Engineering vs Context Engineering. A Hands-on Cortex AI Walkthrough An example demonstrating how the same prompt and instruction return different severity ratings depending on how much …
Stop Paying Your LLM to Flip Coins 9 design patterns for System One models like Jev and Laya, and how they make AI agents faster, cheaper, and safer Here’s an uncomfortable exercise. Open your …
Across 132 graded agent runs on TinyDB, correctness stayed tied while cost, speed, tool use, and code-review tradeoffs split apart.Continue reading on Towards AI »
Your AI Agent Will Obey Anyone. Guardrails Are How You Stop It. A guide to the attacks that break LLMs and AI agents, and the guardrails that stop each one. This guide assumes you know nothing about …
Only 193 of 520 npm Jev clients honour TYPESAFE_BASE_URL, the one setting Ollaya needs to run Jev locally. Official-SDK clients: 86%. Hand-rolled: 12%.
How a poisoned security scanner led to the LiteLLM supply chain attack, what stolen LLM keys cost via LLMjacking, and seven controls that limit the damage.
Suppose you’re building a service that receives customer support tickets. Before assigning a ticket, your code needs to answer two questions: Which team should handle it? Does it need urgent attention? You could ask a language model to write an assessment and then parse its response.
Claude Code makes a lot of small judgments that don't need a full LLM call: does this edit break a rule in my CLAUDE.md, which of my installed skills fits this prompt, does this diff need a careful review or a quick pass.
If you give an LLM access to your code, you write tools. A tool is a function plus what a model needs to call it: a name, a description, and a schema for the arguments. Then you write the same tools again. The first version uses the AI SDK's tool().
Anthropic released Claude Sonnet 5.5 this week, and the main takeaway isn't a new state-of-the-art benchmark. The real story is the focus on cost-per-task for the bulk of everyday engineering work.
I built a role-play game with LLMs and expected werewolves on spaceships and in abandoned castles. Some players had a different idea. What happened I have a little Werewolf text game with LLMs. With a caveat - there is a role-play.
Built for the TigerGraph Agentic GraphRAG Hackathon. Repo and dashboard linked at the end. RAG retrieves text. GraphRAG adds structure. Agentic GraphRAG lets a model plan its own investigation. Everyone assumes the third one is better — but by how much, and on which questions?
The Ghost in the Machine: My Robot Vacuum's Existential Crisis (and Ours) My robot vacuum, Bartholomew, has started avoiding the rug in the living room. Not all the time, just on Tuesdays.
AI phone agents went from obviously robotic to occasionally indistinguishable from a person in about two years. The reason is not one breakthrough. It is that three separate components got good at the same time, and the engineering problem of connecting them got solved well enough.
GPT-6.1 Sol dan Claude Sonnet 5.5 sama-sama berharga $2 per juta token input dan $10 per juta token output. Keduanya juga dirilis selisih satu hari: Sonnet 5.5 pada 28 September 2026, lalu GPT-6.1 Sol pada 29 September.
Untuk memanggil API GPT-6.1 Sol, kirim permintaan POST ke https://api.openai.com/v1/responses dengan "model": "gpt-6.1-sol" dan token Bearer Anda. Harga standar tetap $2 untuk input dan $10 untuk output per juta token, tetapi input yang di-cache turun dari $0,20 menjadi $0,10.
Để gọi API GPT-6.1 Sol, gửi yêu cầu POST đến https://api.openai.com/v1/responses với "model": "gpt-6.1-sol" và API key dưới dạng Bearer token. Giá niêm yết giữ nguyên ở mức 2 đô la đầu vào và 10 đô la đầu ra cho mỗi triệu token như GPT-6 Sol, nhưng đầu vào đã cache giảm từ 0,20 đô la xuống 0,10 đô…
Para chamar a API GPT-6.1 Sol, envie uma requisição POST para https://api.openai.com/v1/responses com "model": "gpt-6.1-sol" e sua chave como token Bearer. O preço permanece em US$ 2 de entrada e US$ 10 de saída por milhão de tokens, como no GPT-6 Sol.
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